Handbook of Regression Modeling in People Analytics - Info and Reading Options
By Keith McNulty
"Handbook of Regression Modeling in People Analytics" was published by Taylor & Francis Group in 2021 - Boca Raton London New York, the book is classified in Mathematical statistics genre, it has 272 pages and the language of the book is English.
“Handbook of Regression Modeling in People Analytics” Metadata:
- Title: ➤ Handbook of Regression Modeling in People Analytics
- Author: Keith McNulty
- Language: English
- Number of Pages: 272
- Is Family Friendly: Yes - No Mature Content
- Publisher: Taylor & Francis Group
- Publish Date: 2021
- Publish Location: Boca Raton London New York
- Genres: Mathematical statistics
“Handbook of Regression Modeling in People Analytics” Subjects and Themes:
- Subjects: ➤ Mathematics - Regression analysis - Mathematical statistics - R (Computer program language) - Python (Computer program language) - Regression Analysis - Analyse de régression - R (Langage de programmation) - Python (Langage de programmation) - BUSINESS & ECONOMICS - Statistics - MATHEMATICS - Probability & Statistics - General
Edition Identifiers:
- Google Books ID: GPNEzgEACAAJ
- The Open Library ID: OL33905919M - OL25316863W
- Online Computer Library Center (OCLC) ID: 1263243189
- ISBN-13: 9781032041742 - 9781003194156
- ISBN-10: 1032041749
- All ISBNs: 9781032041742 - 1032041749 - 9781003194156
AI-generated Review of “Handbook of Regression Modeling in People Analytics”:
Snippets and Summary:
* 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) * Clear step-by-step instructions on executing the analyses. * Clear guidance on how to interpret results. * Primary instruction in R ...
"Handbook of Regression Modeling in People Analytics" Description:
Google Books:
This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling.
Open Data:
1. The Importance of Regression in People Analytics. 2. The Basics of the R Programming Language. 3. Statistics Foundations. 4. Linear Regression for Continuous Outcomes. 5. Binomial Logistic Regression for Binary Outcomes. 6. Multinomial Logistic Regression for Nominal Category Outcomes. 7. Ordinal Logistic Regression for Ordered Category Outcomes. 8. Modeling Explicit and Latent Hierarchical Structure in Data. 9. Survival Analysis for Modeling the Occurrence of Singular Events Over Time. 10. Alternative Technical Approaches in R and Python. 11. Power Analysis to Estimate Required Sample Sizes for Inferential Modeling. 12. Further Exercises for Practice
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